A Decision-Theoretic Framework for Comparing Likelihood Ratio Methods for the Rare Type Match Problem
The rare type match problem is a challenging situation faced by a forensic statistician who aims at providing the value of a match between some characteristic of a crime stain and the corresponding characteristic of a suspect's stain when this characteristic has not been observed before. Several methods have been designed in the literature to assess likelihood ratios for the rare type match case when evidence consists of a Y-STR profile found on the crime scene matching the Y-STR profile of a designated suspect. We develop a general Bayesian decision-theoretic framework for quantifying the expected cost of alternative approaches using the logarithmic scoring rule. The framework provides a novel formalization of the posterior cross-entropy, which explicitly enhances the contribution of the method-specific strategy used to extract information from the data. We revisit existing decompositions of posterior cross-entropy and introduce a new complementary decomposition that provides a more interpretable characterization of this contribution. This work compares nine different methods by assessing their performance using empirical validation experiments. Its ultimate goal is to provide forensic experts and triers of fact with methodological insight and a pre-experimental guide to these alternative approaches.